Trachea_WT_10x
This script analyze 10x scRNA-seq data from E15, E16, P1, and P4 wt mice
library(dplyr)
library(Matrix)
require(devtools)
install_version("mvtnorm", version = "1.0-8", repos = "http://cran.us.r-project.org") ##Note that I need version 1.0-8 because the newest version requires R>3.5 (my r version is 3.4). mvtnorm is a dependency for fpc which is a dependency for Seurat.
library(Seurat)First load E15 data (nGene 1k, nUMI 5k already applied)
load("E15_Oct10_10X_Trachea.RData")
E15_Oct10_mm10.1.2.0_Trachea <- SetAllIdent(object = E15_Oct10_mm10.1.2.0_Trachea, id = "genotype")
second load seu_E16P1P4_wt.RData
load("seu_E16P1P4_wt.RData")data has been log normalized.
seurat_E15E16P1P4_wt<-MergeSeurat(object1 = seu_E16P1P4_wt,object2 = SubsetData(object=E15_Oct10_mm10.1.2.0_Trachea,ident.use=c("wt")),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18")Alternatively, seurat_E15E16P1P4_wt object can be created by merging 4 seurat objects in E15_Oct10_10X_Trachea.RData, E16_Dec7v3_Trachea.RData, seu_P1_Dec11_mm10.1.2.0.RData, and P4_10X_mm10_1.2.0.RData
remove old analysis done on the E16P1P4 dataset:
seurat_E15E16P1P4_wt@meta.data<-seurat_E15E16P1P4_wt@meta.data[,-which(names(seurat_E15E16P1P4_wt@meta.data) %in% c("res.0.8", "res.1.2","res.1.4","cell_type","S.Score","G2M.Score","Phase","old.ident","CellCycle_score","mocosaGoblet_score","ciliopathy_score","PCD_score"))]Basic stats about the data:
median(seurat_E15E16P1P4_wt@meta.data$nGene)
[1] 2979mean(seurat_E15E16P1P4_wt@meta.data$nGene)
[1] 3220.857median(seurat_E15E16P1P4_wt@meta.data$nUMI)
[1] 9872mean(seurat_E15E16P1P4_wt@meta.data$nUMI)
[1] 12465.92table(seurat_E15E16P1P4_wt@meta.data$age)
E15 E16 P1 P4
4398 8432 2270 1709 table(seurat_E15E16P1P4_wt@meta.data$seq_group)
E15_Oct10_wt_green E15_Oct10_wt_red E16_Dec7_wt_1 E16_Dec7_wt_6 P1_Dec11_wt_6 P1_Dec11_wt_7
2806 1592 3338 5094 1110 1160
P4_Oct18_wt_green P4_Oct18_wt_red
803 906 scale the data:
seurat_E15E16P1P4_wt <- ScaleData(object = seurat_E15E16P1P4_wt)find variable genes and run PCA:
seurat_E15E16P1P4_wt <- FindVariableGenes(object = seurat_E15E16P1P4_wt, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)seurat_E15E16P1P4_wt <- RunPCA(object = seurat_E15E16P1P4_wt,pcs.compute = 25, do.print = FALSE)
seurat_E15E16P1P4_wt <- ProjectPCA(object = seurat_E15E16P1P4_wt, do.print = FALSE)
PCElbowPlot(object = seurat_E15E16P1P4_wt,num.pc = 25)clustering:
try resolution=0.8:
n.pcs = 24
res.used <- 0.8
seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)
resolution=1.4:
n.pcs = 24
res.used <- 1.4
seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.k.param=15 to have better distinction for rare cells:
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.4")
res1.6 leads to further specification of immune cells, P1 and P4 mesenchymal progenitor separation, and separation of E16 basal:
n.pcs = 24
res.used <- 1.6
seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.6")We will use resolution of 1.4 for further analysis.
——————————————————————–
#save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData") # this seurat object will be updated and saved as analysis goes#load("seuratE15E16P1P4_wt.RData")
2 E16 wt mice:
2 P1 wt mice:
Annotate clusters by known markers:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"),
slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=30,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28,14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25,24,32)
)
cluster annotation v1:
library(plyr)
seurat_E15E16P1P4_wt@meta.data$cell_type<-mapvalues(seurat_E15E16P1P4_wt@meta.data$res.1.4,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33"),to=c("Fibroblast","Basal","Secretory","Basal","Fibroblast","Fibroblast","Fibroblast","Secretory","CyclingEpithelial","Transitional","Fibroblast","CyclingEpithelial","Fibroblast","Fibroblast","Immune","Fibroblast","Fibroblast","Fibroblast","Muscle","Fibroblast","Ciliated","Muscle","MesenchymalProgenitor","Endothelial","Thyroid","MesenchymalProgenitor","Neural/Schwann","Ciliated","CiliaSecretory","Basal","CyclingEpithelial","Endothelial","Doublet","MesenchymalProgenitor"))use nonShh subset clustering information (seurat object: E15E16P1P4_wt_nonShh resolution=1.6) to annotate those non-epithelial cells:
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_nonShh_cellType1.6, col.name = "specific_type")table(seurat_E15E16P1P4_wt@meta.data$specific_type)
Chondrocyte CyclingFibroblast Fibroblast Immune_1 Immune_2
956 1516 4688 347 166
LymphaticEndothelial MesenchymalProgenitor Muscle Neuron/NEC RBC
134 511 720 51 59
SchwannCell VascularEndothelial VSMC/pericyte
94 268 80 keep the epithelial cell annotation as in seurat_E15E16P1P4_wt@meta.data$cell_type:
seurat_E15E16P1P4_wt@meta.data$specific_type <- ifelse(is.na(seurat_E15E16P1P4_wt@meta.data$specific_type), as.character(seurat_E15E16P1P4_wt@meta.data$cell_type), as.character(seurat_E15E16P1P4_wt@meta.data$specific_type))now we have new (more detailed) annotation for all cells:
table(seurat_E15E16P1P4_wt@meta.data$specific_type)
Basal Chondrocyte CiliaSecretory Ciliated CyclingEpithelial
2491 956 196 571 1297
CyclingFibroblast Doublet Fibroblast Immune_1 Immune_2
1516 89 4688 347 166
LymphaticEndothelial MesenchymalProgenitor Muscle Neuron/NEC RBC
134 511 720 51 59
SchwannCell Secretory Thyroid Transitional VascularEndothelial
94 1729 245 601 268
VSMC/pericyte
80 table(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$gate,useNA = "always")
green red <NA>
Basal 1106 1 1384
Chondrocyte 4 327 625
CiliaSecretory 76 0 120
Ciliated 202 2 367
CyclingEpithelial 1160 3 134
CyclingFibroblast 4 374 1138
Doublet 0 0 89
Fibroblast 27 833 3828
Immune_1 0 72 275
Immune_2 0 46 120
LymphaticEndothelial 0 33 101
MesenchymalProgenitor 7 192 312
Muscle 1 251 468
Neuron/NEC 0 36 15
RBC 0 1 58
SchwannCell 0 36 58
Secretory 412 1 1316
Thyroid 8 213 24
Transitional 601 0 0
VascularEndothelial 0 67 201
VSMC/pericyte 1 10 69
<NA> 0 0 0specific_type of each cell is defined later.
save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")
find differentially expressed genes between clusters of interest:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
E15E16P1P4_wt_res14_BasalSecretory<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(9,30),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~02m 44s
|++ | 2 % ~02m 46s
|++ | 3 % ~02m 41s
|+++ | 4 % ~02m 38s
|+++ | 6 % ~02m 34s
|++++ | 7 % ~02m 32s
|++++ | 8 % ~02m 33s
|+++++ | 9 % ~02m 31s
|++++++ | 10% ~02m 29s
|++++++ | 11% ~02m 26s
|+++++++ | 12% ~02m 24s
|+++++++ | 13% ~02m 22s
|++++++++ | 15% ~02m 20s
|++++++++ | 16% ~02m 22s
|+++++++++ | 17% ~02m 20s
|+++++++++ | 18% ~02m 17s
|++++++++++ | 19% ~02m 15s
|+++++++++++ | 20% ~02m 13s
|+++++++++++ | 21% ~02m 10s
|++++++++++++ | 22% ~02m 08s
|++++++++++++ | 24% ~02m 06s
|+++++++++++++ | 25% ~02m 04s
|+++++++++++++ | 26% ~02m 02s
|++++++++++++++ | 27% ~02m 00s
|+++++++++++++++ | 28% ~01m 58s
|+++++++++++++++ | 29% ~01m 56s
|++++++++++++++++ | 30% ~01m 54s
|++++++++++++++++ | 31% ~01m 53s
|+++++++++++++++++ | 33% ~01m 51s
|+++++++++++++++++ | 34% ~01m 49s
|++++++++++++++++++ | 35% ~01m 47s
|++++++++++++++++++ | 36% ~01m 45s
|+++++++++++++++++++ | 37% ~01m 44s
|++++++++++++++++++++ | 38% ~01m 42s
|++++++++++++++++++++ | 39% ~01m 40s
|+++++++++++++++++++++ | 40% ~01m 39s
|+++++++++++++++++++++ | 42% ~01m 37s
|++++++++++++++++++++++ | 43% ~01m 35s
|++++++++++++++++++++++ | 44% ~01m 33s
|+++++++++++++++++++++++ | 45% ~01m 31s
|++++++++++++++++++++++++ | 46% ~01m 29s
|++++++++++++++++++++++++ | 47% ~01m 27s
|+++++++++++++++++++++++++ | 48% ~01m 25s
|+++++++++++++++++++++++++ | 49% ~01m 23s
|++++++++++++++++++++++++++ | 51% ~01m 21s
|++++++++++++++++++++++++++ | 52% ~01m 19s
|+++++++++++++++++++++++++++ | 53% ~01m 17s
|+++++++++++++++++++++++++++ | 54% ~01m 16s
|++++++++++++++++++++++++++++ | 55% ~01m 14s
|+++++++++++++++++++++++++++++ | 56% ~01m 12s
|+++++++++++++++++++++++++++++ | 57% ~01m 10s
|++++++++++++++++++++++++++++++ | 58% ~01m 08s
|++++++++++++++++++++++++++++++ | 60% ~01m 06s
|+++++++++++++++++++++++++++++++ | 61% ~01m 05s
|+++++++++++++++++++++++++++++++ | 62% ~01m 03s
|++++++++++++++++++++++++++++++++ | 63% ~01m 01s
|+++++++++++++++++++++++++++++++++ | 64% ~59s
|+++++++++++++++++++++++++++++++++ | 65% ~57s
|++++++++++++++++++++++++++++++++++ | 66% ~56s
|++++++++++++++++++++++++++++++++++ | 67% ~54s
|+++++++++++++++++++++++++++++++++++ | 69% ~52s
|+++++++++++++++++++++++++++++++++++ | 70% ~50s
|++++++++++++++++++++++++++++++++++++ | 71% ~49s
|++++++++++++++++++++++++++++++++++++ | 72% ~47s
|+++++++++++++++++++++++++++++++++++++ | 73% ~45s
|++++++++++++++++++++++++++++++++++++++ | 74% ~43s
|++++++++++++++++++++++++++++++++++++++ | 75% ~41s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~39s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~37s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~35s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~33s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~32s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~30s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~28s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~26s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~24s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 46sE15E16P1P4_wt_res14_BasalSecretoryFor the purpose of visualization, we average within each specific cell type:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
average_wt_specific_Annotation<-AverageExpression(object = seurat_E15E16P1P4_wt,return.seurat = T)Finished averaging RNA for cluster Basal
Finished averaging RNA for cluster Chondrocyte
Finished averaging RNA for cluster CiliaSecretory
Finished averaging RNA for cluster Ciliated
Finished averaging RNA for cluster CyclingEpithelial
Finished averaging RNA for cluster CyclingFibroblast
Finished averaging RNA for cluster Doublet
Finished averaging RNA for cluster Fibroblast
Finished averaging RNA for cluster Immune_1
Finished averaging RNA for cluster Immune_2
Finished averaging RNA for cluster LymphaticEndothelial
Finished averaging RNA for cluster MesenchymalProgenitor
Finished averaging RNA for cluster Muscle
Finished averaging RNA for cluster Neuron/NEC
Finished averaging RNA for cluster RBC
Finished averaging RNA for cluster SchwannCell
Finished averaging RNA for cluster Secretory
Finished averaging RNA for cluster Thyroid
Finished averaging RNA for cluster Transitional
Finished averaging RNA for cluster VascularEndothelial
Finished averaging RNA for cluster VSMC/pericyte
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Scaling data matrix
|
| | 0%
|
|==============================================================================================================| 100%
print(levels(seurat_E15E16P1P4_wt@ident))
epithlial cells across ages:
df_wt<-FetchData(seurat_E15E16P1P4_wt,c("Ano1","Cftr","Krt4","Krt13","res.1.4","age","seq_group","cell_type","specific_type"))
any possible ionocytes?:
table(df_all_wt$res.1.4[df_all_wt$Foxi1>0])
0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 4 5 6 7 8 9
0 0 0 0 0 0 2 0 1 1 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 table(df_all_wt$res.1.4[df_all_wt$Cftr>0 & df_all_wt$Foxi1>0])
0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 4 5 6 7 8 9
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 epithelial cell composition across ages:
table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)],seurat_E15E16P1P4_wt@meta.data$cell_type[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)])
Basal CiliaSecretory Ciliated CyclingEpithelial Secretory Transitional
E15 1017 0 1 1155 0 601
E16 1324 3 294 132 1094 0
P1 60 117 73 2 222 0
P4 90 76 203 8 413 0a few lineage markers:
seurat_E15E16P1P4_wt=buildClusterTree(seurat_E15E16P1P4_wt,do.reorder = F,reorder.numeric = F,pcs.use = 1:24)
'buildClusterTree' is deprecated.
Use 'BuildClusterTree' instead.
See help("Deprecated") and help("Seurat-deprecated").Finished averaging RNA for cluster 0
Finished averaging RNA for cluster 1
Finished averaging RNA for cluster 2
Finished averaging RNA for cluster 3
Finished averaging RNA for cluster 4
Finished averaging RNA for cluster 5
Finished averaging RNA for cluster 6
Finished averaging RNA for cluster 7
Finished averaging RNA for cluster 8
Finished averaging RNA for cluster 9
Finished averaging RNA for cluster 10
Finished averaging RNA for cluster 11
Finished averaging RNA for cluster 12
Finished averaging RNA for cluster 13
Finished averaging RNA for cluster 14
Finished averaging RNA for cluster 15
Finished averaging RNA for cluster 16
Finished averaging RNA for cluster 17
Finished averaging RNA for cluster 18
Finished averaging RNA for cluster 19
Finished averaging RNA for cluster 20
Finished averaging RNA for cluster 21
Finished averaging RNA for cluster 22
Finished averaging RNA for cluster 23
Finished averaging RNA for cluster 24
Finished averaging RNA for cluster 25
Finished averaging RNA for cluster 26
Finished averaging RNA for cluster 27
Finished averaging RNA for cluster 28
Finished averaging RNA for cluster 29
Finished averaging RNA for cluster 30
Finished averaging RNA for cluster 31
Finished averaging RNA for cluster 32
Finished averaging RNA for cluster 33
potential mesenchymal progenitors:
E15E16P1P4_wt_res14_22over33<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(22),ident.2 = c(33),only.pos = TRUE)
E15E16P1P4_wt_res14_22over33E15E16P1P4_wt_res14_33over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(33),ident.2 = c(22),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~13s
|++++ | 6 % ~12s
|++++ | 8 % ~11s
|+++++ | 9 % ~10s
|+++++ | 10% ~10s
|++++++ | 11% ~09s
|++++++ | 12% ~09s
|+++++++ | 13% ~09s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 20% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~06s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08sE15E16P1P4_wt_res14_33over22E15E16P1P4_wt_res14_25over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(25),ident.2 = c(22),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 11% ~04s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~03s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05sE15E16P1P4_wt_res14_25over22
scGPS (scoring):
head(geneList$Mucociliary)
[1] "Fcgr3" "Dnah7c" "Dnah7b" "Dnah7a" "Mst1" "Mst1r" library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:plyr’:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionpercentile_table_E15E16P1P4wt<-apply(seurat_E15E16P1P4_wt@data,1,percent_rank)percentile_table_E15E16P1P4wt[1:6,1:6] Xkr4 Gm1992 Gm37381 Rp1 Sox17 Gm37323
E16_Dec7_wt_1_AAACCTGAGATCCCGC 0 0 0 0.0000000 0 0
E16_Dec7_wt_1_AAACCTGAGGTGCAAC 0 0 0 0.0000000 0 0
E16_Dec7_wt_1_AAACCTGGTAAATGAC 0 0 0 0.0000000 0 0
E16_Dec7_wt_1_AAACCTGGTGATGCCC 0 0 0 0.0000000 0 0
E16_Dec7_wt_1_AAACCTGTCACATACG 0 0 0 0.0000000 0 0
E16_Dec7_wt_1_AAACCTGTCAGCGACC 0 0 0 0.9869705 0 0 CellCycle_score_wt<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Cell_cycle],1,mean) head( CellCycle_score_wt)E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC
0.04833364 0.10794933 0.06216909
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC
0.66890828 0.10226353 0.36390747 seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = CellCycle_score_wt, col.name = "CellCycle_score")VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("CellCycle_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")
wt_mocosaGoblet_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],1,mean)seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_mocosaGoblet_score, col.name = "mucosaGoblet_score")VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")
wt_ciliopathy_table<- percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Ciliopathy] wt_ciliopathy_score<- apply(wt_ciliopathy_table,1,mean) head(wt_ciliopathy_score)seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_ciliopathy_score, col.name = "ciliopathy_score")
wt_PCD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Primary.ciliary.dyskinesia],1,mean) head(wt_PCD_score)E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC
0.07178531 0.06811267 0.05672251
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC
0.09034630 0.03588221 0.66970120 seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_PCD_score, col.name = "PCD_score")VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")
ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory"),],aes(age,mucosaGoblet_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1),
geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
wt_bronchitis_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Bronchiectasis.and.Bronchitis],1,mean) head(wt_bronchitis_score)E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC E16_Dec7_wt_1_AAACCTGGTGATGCCC
0.05980333 0.12838142 0.10012107 0.07827476
E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC
0.09195436 0.36792222 seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_bronchitis_score, col.name = "Bronchiectasis_Bronchitis")
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))
wt_COPD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$COPD],1,mean) head(wt_COPD_score)E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC
0.1345453 0.1974387 0.1539624
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC
0.1719776 0.1320101 0.2590983 seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_COPD_score, col.name = "COPD")
wt_asthma_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Pulmonary...Asthma],1,mean) head(wt_asthma_score)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC
0.08485814 0.13134618 0.09996662
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC
0.09764181 0.08522185 0.14710912 seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_asthma_score, col.name = "Asthma")
check some human GWAS:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Apip","Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)
CF GWAS:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Aplp1","Aplp2","H2-Aa","H2-Ab1","H2-Eb1","H2-Eb2"),group.by = "ident", x.lab.rot = T,plot.legend = T)
COPD:
Developmental lanscape:
basal cells across time:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
E15E16P1P4_wt_res14_3over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(3),ident.2 = c(1),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~30s
|++ | 2 % ~28s
|++ | 3 % ~27s
|+++ | 4 % ~26s
|+++ | 5 % ~25s
|++++ | 6 % ~25s
|++++ | 7 % ~24s
|+++++ | 8 % ~24s
|+++++ | 9 % ~24s
|++++++ | 10% ~23s
|++++++ | 11% ~28s
|+++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~26s
|++++++++ | 15% ~26s
|+++++++++ | 16% ~25s
|+++++++++ | 17% ~25s
|++++++++++ | 18% ~24s
|++++++++++ | 19% ~24s
|+++++++++++ | 20% ~23s
|+++++++++++ | 21% ~23s
|++++++++++++ | 22% ~22s
|++++++++++++ | 23% ~22s
|+++++++++++++ | 24% ~22s
|+++++++++++++ | 26% ~21s
|++++++++++++++ | 27% ~21s
|++++++++++++++ | 28% ~21s
|+++++++++++++++ | 29% ~20s
|+++++++++++++++ | 30% ~20s
|++++++++++++++++ | 31% ~20s
|++++++++++++++++ | 32% ~19s
|+++++++++++++++++ | 33% ~19s
|+++++++++++++++++ | 34% ~19s
|++++++++++++++++++ | 35% ~18s
|++++++++++++++++++ | 36% ~18s
|+++++++++++++++++++ | 37% ~18s
|+++++++++++++++++++ | 38% ~17s
|++++++++++++++++++++ | 39% ~17s
|++++++++++++++++++++ | 40% ~17s
|+++++++++++++++++++++ | 41% ~16s
|+++++++++++++++++++++ | 42% ~16s
|++++++++++++++++++++++ | 43% ~16s
|++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~15s
|+++++++++++++++++++++++ | 46% ~15s
|++++++++++++++++++++++++ | 47% ~15s
|++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~14s
|+++++++++++++++++++++++++ | 50% ~14s
|++++++++++++++++++++++++++ | 51% ~13s
|+++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~13s
|++++++++++++++++++++++++++++ | 54% ~13s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~12s
|+++++++++++++++++++++++++++++ | 57% ~12s
|++++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~11s
|+++++++++++++++++++++++++++++++ | 60% ~11s
|+++++++++++++++++++++++++++++++ | 61% ~11s
|++++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|+++++++++++++++++++++++++++++++++ | 64% ~10s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|++++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~09s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|+++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|++++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 27sE15E16P1P4_wt_res14_3over1write.table(E15E16P1P4_wt_res14_3over1,"E15E16P1P4_wt_res14_3over1.txt",sep="\t")E15E16P1P4_wt_res14_1over3<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(1),ident.2 = c(3),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~14s
|++ | 3 % ~14s
|+++ | 5 % ~14s
|+++ | 6 % ~14s
|++++ | 7 % ~14s
|++++ | 8 % ~14s
|+++++ | 9 % ~14s
|++++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 14% ~13s
|++++++++ | 15% ~13s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 18% ~12s
|++++++++++ | 19% ~12s
|+++++++++++ | 20% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|+++++++++++++++ | 28% ~11s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~08s
|+++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~06s
|+++++++++++++++++++++++++++++ | 58% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|+++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 15sE15E16P1P4_wt_res14_1over3write.table(E15E16P1P4_wt_res14_1over3,"E15E16P1P4_wt_res14_1over3.txt",sep="\t")E15E16P1P4_wt_res14_29over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(29),ident.2 = c(1),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~09s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 09sE15E16P1P4_wt_res14_29over1
df_3_1_29_wt<-FetchData(seurat_E15E16P1P4_wt,c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt4","Krt5","Krt15","Aqp3","Aqp4","Aqp5","Rpl6l","Rpl9-ps6","Rpl13-ps3","age","res.1.4"))
df_3_1_29_wt<-df_3_1_29_wt[order(factor(df_3_1_29_wt$res.1.4,levels=c("3","1","29")),df_3_1_29_wt$age),]age<-substr(rownames(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),]),1,3)table(seurat_E15E16P1P4_wt@meta.data$res.1.4)
0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27
1347 1324 557 552 531 506 493 474 462 448 395 388 1094 362 342 317 272 245 222 221 209
28 29 3 30 31 32 33 4 5 6 7 8 9
196 149 1018 137 134 89 88 916 744 733 635 608 601 library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c("E15", "E16", "P1","P4"), # category labels
col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c("E15", "E16", "P1","P4"), # category labels
col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
pdf(file = paste("Manuscript/heatmap/","basal_dev",".pdf", sep = ""), width = 7, height = 3)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.Error in plot.new() : figure margins too largeggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29),],aes(res.1.4,fill=age))+
geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))+scale_x_discrete(limits=c("3","1","29"))
ciliated cells across time:
E15E16P1P4_wt_res14_27over20<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(27),ident.2 = c(20),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~12s
|++ | 2 % ~12s
|++ | 3 % ~11s
|+++ | 4 % ~11s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~11s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~10s
|+++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|+++++++++++ | 20% ~10s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 12sE15E16P1P4_wt_res14_27over20write.table(E15E16P1P4_wt_res14_27over20,"E15E16P1P4_wt_res14_27over20.txt",sep="\t")
E15E16P1P4_wt_res14_20over27<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(20),ident.2 = c(27),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 2 % ~13s
|++ | 3 % ~11s
|+++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|++++ | 8 % ~10s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|++++++ | 12% ~09s
|+++++++ | 13% ~08s
|+++++++ | 14% ~09s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~06s
|+++++++++++++++++ | 33% ~06s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 09sE15E16P1P4_wt_res14_20over27write.table(E15E16P1P4_wt_res14_20over27,"E15E16P1P4_wt_res14_20over27.txt",sep="\t")
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)
0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27
1347 1324 557 552 531 506 493 474 462 448 395 388 1094 362 342 317 272 245 222 221 209
28 29 3 30 31 32 33 4 5 6 7 8 9
196 149 1018 137 134 89 88 916 744 733 635 608 601 df_20_27_wt<-FetchData(seurat_E15E16P1P4_wt,c("Foxj1","Lrrc23","Ccdc67","Cep152","Plk4","Ccna1","Ccno","Cdc20b","Cdc20","Hyls1","Mcidas","Smim24","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Ly6a","Ly6c1","Adam8","Cxcl17","Ifitm1","age","res.1.4"))
df_20_27_wt<-df_20_27_wt[order(df_20_27_wt$res.1.4,df_20_27_wt$age),]age<-substr(rownames(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),]),1,3)library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:25])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c("E15", "E16", "P1","P4"), # category labels
col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:22])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c("E15", "E16", "P1","P4"), # category labels
col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
table(seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)])prop.table(table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)]),1)
20 27
E15 1.00000000 0.00000000
E16 0.98979592 0.01020408
P1 0.19178082 0.80821918
P4 0.27586207 0.72413793
secretory cells across time:
E15E16P1P4_wt_res14_2over7<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(2),ident.2 = c(7),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~16s
|++ | 2 % ~16s
|++ | 3 % ~16s
|+++ | 4 % ~15s
|+++ | 5 % ~15s
|++++ | 6 % ~15s
|++++ | 7 % ~15s
|+++++ | 8 % ~15s
|+++++ | 9 % ~14s
|++++++ | 10% ~14s
|++++++ | 11% ~14s
|+++++++ | 12% ~14s
|+++++++ | 13% ~14s
|++++++++ | 14% ~14s
|++++++++ | 15% ~14s
|+++++++++ | 16% ~13s
|+++++++++ | 17% ~13s
|++++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|+++++++++++ | 20% ~13s
|+++++++++++ | 21% ~13s
|++++++++++++ | 22% ~12s
|++++++++++++ | 23% ~12s
|+++++++++++++ | 24% ~12s
|+++++++++++++ | 25% ~12s
|++++++++++++++ | 26% ~12s
|++++++++++++++ | 27% ~12s
|+++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~11s
|++++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~11s
|+++++++++++++++++ | 32% ~11s
|+++++++++++++++++ | 33% ~11s
|++++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|+++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~10s
|++++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|+++++++++++++++++++++ | 40% ~10s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|+++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|++++++++++++++++++++++++ | 46% ~09s
|++++++++++++++++++++++++ | 47% ~08s
|+++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~08s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~07s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 16sE15E16P1P4_wt_res14_2over7E15E16P1P4_wt_res14_7over2<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(7),ident.2 = c(2),only.pos = TRUE)
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06sE15E16P1P4_wt_res14_7over2
df_2_7_wt<-FetchData(seurat_E15E16P1P4_wt,c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1","age","res.1.4"))
df_2_7_wt<-df_2_7_wt[order(df_2_7_wt$res.1.4,df_2_7_wt$age),]age<-substr(rownames(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),]),1,3)table(seurat_E15E16P1P4_wt@meta.data$res.1.4)
0 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27
1347 1324 557 552 531 506 493 474 462 448 395 388 1094 362 342 317 272 245 222 221 209
28 29 3 30 31 32 33 4 5 6 7 8 9
196 149 1018 137 134 89 88 916 744 733 635 608 601 library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c( "E16", "P1","P4"), # category labels
col = c("#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.par(lend = 1) # square line ends for the color legendlegend("left", # location of the legend on the heatmap plot
legend = c( "E16", "P1","P4"), # category labels
col = c("#2ca25f","#66c2a4","#99d8c9"), # color key
lty= 1, # line style
lwd = 10 # line width
)
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7),],aes(res.1.4,fill=age))+
geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_x_discrete(limits=c("2","7"))+
scale_fill_manual(values = c("#2ca25f","#66c2a4","#99d8c9"))
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Foxj1","Mcidas","Cdhr3","Creb3l1","Spdef","Gp2"), nCol = 3,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )
immune response:
Toll like receptors and signaling:
CLRs (C-type lectin domain):
NLR:
RLR:
df_all_wt<-FetchData(seurat_E15E16P1P4_wt,c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1","res.1.4","age","seq_group","cell_type","Foxj1","Spdef","Foxa3","Creb3l1","Gp2","Tff2","Scgb1a1","Cftr","Foxi1","Gja1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4","Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1","Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg","Il2","Il17b","Il17d","Il34","Il11","Il15","Ifnk","Ifnlr1","Ifkbiz","Ifkbia"))
Error in FetchData(seurat_E15E16P1P4_wt, c("Lbp", "Cd14", "Tlr4", "Tlr2", :
Error: Ifkbiz not foundtable(colnames(df_all_wt))
age Alox15 Alox5 Areg Ccl11 Ccl17 Ccl2 Ccl20 Ccl21a Ccl24 Ccl25 Ccl27a Ccl28 Ccl5 Ccl7 Cd14
1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
cell_type Cftr Chil4 Creb3l1 Cx3cl1 Cxcl1 Cxcl10 Cxcl12 Cxcl14 Cxcl15 Cxcl16 Cxcl17 Cxcl2 Ddx58 Defb1 Dhx58
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
F2rl1 Foxa3 Foxi1 Foxj1 Gata2 Gja1 Gp2 Hpgds Ifih1 Il10 Il18 Il1b Il1rl1 Il25 Il33 Il6
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Itln1 Lbp Lcn2 Lgals3 Ltf Lyz2 Muc1 Muc16 Muc2 Muc20 Muc4 Muc5ac Muc5b Myd88 Nlrp6 Nod1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Nod2 Ormdl3 Pf4 Pigr Ptgds Ptges Ptgs2 Reg3g res.1.4 Retnla S100a8 S100a9 Scgb1a1 seq_group Sftpa1 Sftpb
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Sftpd Slpi Spdef Sucnr1 Tbxas1 Tff2 Tgfb1 Tgfb2 Ticam1 Tlr2 Tlr4 Tnf Tslp
1 1 1 1 1 1 1 1 1 1 1 1 1 #for (i in c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1"))
#{
#pdf(file = paste("Manuscript/MicrobialSensing/",i,".pdf", sep = ""), width = 7, height = 3)
#print(ggplot(df_all_wt[(df_all_wt$specific_type %in% #c("Basal","Secretory","Ciliated","Transitional","CiliaSecretory","CyclingEpithelial")),],aes_string(x="age",y=i))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.#01,stackdir="center",position=position_dodge(0.8), dotsize=0.8)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1),
# geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1)))
#dev.off()
#}Some categories of genes:
lipid synthetic enzymes or inhibitors: “Ormdl3”,“Ptges”,“Ptgds”,“Ptgs2”,“Hpgds”,“Hpgds”,“Ptgr2”,“Tbxas1”,“Alox5”,“Ptgdr”
known Th2 inducers, effectors, and mediators:“Il10”,“Tnf”,“S100a8”,“S100a9”,“Il6”,“Il18”,“Il1b”,“Il1rl1”,“Ccl11”,“Ccl24”,“Il33”,“Il25”,“Tslp”,“F2rl1”,“Retnla”,“Alox15”,“Alox5”,“Gata2”,“Tgfb2”,“Tgfb1”,“Ormdl3”,“Ptges”,“Ptgds”,“Ptgs2”,“Hpgds”,“Tbxas1”,“Areg”
chemokines: Pbpb not expressed, Ccl21c, Ccl21c.1, Cxcl11 not found in mm10.1.2.0. “Cxcl3”,“Cxcl2”,“Cxcl1”,“Pf4”,“Cxcl5”,“Cxcl9”,“Cxcl10”,“Cxcl12”,“Cxcl13”,“Cxcl14”,“Cxcl15”,“Cxcl16”,“Cxcl17”,“Ccl1”,“Ccl2”,“Ccl3”,“Ccl4”,“Ccl5”,“Ccl7”,“Ccl8”,“Ccl11”,“Ccl12”,“Ccl9”,“Ccl17”,“Ccl19”,“Ccl20”,“Ccl21a”,“Ccl21b.1”,“Ccl22”,“Ccl6”,“Ccl24”,“Ccl25”,“Ccl27a”,“Ccl27b”,“Ccl28”,“Xcl1”,“Cx3cl1”
expressed chemokines: “Ccl5”,“Cxcl10”,“Cxcl2”,“Cxcl1”,“Pf4”,“Cxcl12”,“Cxcl14”,“Cxcl15”,“Cxcl16”,“Cxcl17”,“Ccl2”,“Ccl7”,“Ccl17”,“Ccl20”,“Ccl21a”,“Ccl25”,“Ccl27a”,“Ccl28”,“Cx3cl1”
Interleukins (Il3,Il9,Il20,Il31 not expressed):“Il10”,“Il11”,“Il12a”,“Il12b”,“Il13”,“Il15”,“Il16”,“Il17a”,“Il17b”,“Il17c”,“Il17d”,“Il17f”,“Il18”,“Il19”,“Il2”,“Il21”,“Il22”,“Il24”,“Il25”,“Il27”,“Il33”,“Il34”,“Il4”,“Il5”,“Il6”,“Il7”,“Il1a”,“Il1b”
Antimicrobial effectors: “Muc1”,“Muc4”,“Muc16”,“Muc20”,“Muc5b”,“Muc5ac”,“Muc2”,“Defb1”,“Lyz2”,“Ltf”,“Sftpa1”,“Sftpd”,“Sftpb”,“Slpi”,“Lcn2”,“Pigr”,“Chil4”
known epithelial cytokines and mediators: “Il1a”,“Il25”,“Il33”,“Tslp”,“Csf2”,“Ccl5”,“Cxcl10”
Most interferons cannot be found in our dataset:
seurat_E15E16P1P4_wt@meta.data$type_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$cell_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))seurat_E15E16P1P4_wt@meta.data$specificType_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g"),group.by = "ident", x.lab.rot = T,plot.legend = T)
print(levels(seurat_E15E16P1P4_wt@ident))
[1] "Basal_E15" "Basal_E16" "Basal_P1" "Basal_P4"
[5] "Chondrocyte_E15" "Chondrocyte_E16" "Chondrocyte_P1" "Chondrocyte_P4"
[9] "CiliaSecretory_E16" "CiliaSecretory_P1" "CiliaSecretory_P4" "Ciliated_E15"
[13] "Ciliated_E16" "Ciliated_P1" "Ciliated_P4" "CyclingEpithelial_E15"
[17] "CyclingEpithelial_E16" "CyclingEpithelial_P1" "CyclingEpithelial_P4" "CyclingFibroblast_E15"
[21] "CyclingFibroblast_E16" "CyclingFibroblast_P1" "CyclingFibroblast_P4" "Doublet_E16"
[25] "Doublet_P1" "Fibroblast_E15" "Fibroblast_E16" "Fibroblast_P1"
[29] "Fibroblast_P4" "Immune_1_E15" "Immune_1_E16" "Immune_1_P1"
[33] "Immune_1_P4" "Immune_2_E15" "Immune_2_E16" "Immune_2_P1"
[37] "Immune_2_P4" "LymphaticEndothelial_E15" "LymphaticEndothelial_E16" "LymphaticEndothelial_P1"
[41] "LymphaticEndothelial_P4" "MesenchymalProgenitor_E15" "MesenchymalProgenitor_E16" "MesenchymalProgenitor_P1"
[45] "MesenchymalProgenitor_P4" "Muscle_E15" "Muscle_E16" "Muscle_P1"
[49] "Muscle_P4" "Neuron/NEC_E15" "Neuron/NEC_E16" "Neuron/NEC_P4"
[53] "RBC_E16" "RBC_P1" "RBC_P4" "SchwannCell_E15"
[57] "SchwannCell_E16" "SchwannCell_P1" "Secretory_E16" "Secretory_P1"
[61] "Secretory_P4" "Thyroid_E15" "Thyroid_E16" "Thyroid_P1"
[65] "Thyroid_P4" "Transitional_E15" "VascularEndothelial_E15" "VascularEndothelial_E16"
[69] "VascularEndothelial_P1" "VascularEndothelial_P4" "VSMC/pericyte_E15" "VSMC/pericyte_E16"
[73] "VSMC/pericyte_P1" "VSMC/pericyte_P4" DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("gray","forestgreen"),genes.plot = rev(c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g")),group.by = "ident", x.lab.rot = T,plot.legend = T,do.return = T)+rotate()+ theme(axis.text.x = element_text(angle = 45, vjust = 1,hjust=1)) #this scales both genotypes together
Factor `id` contains implicit NA, consider using `forcats::fct_explicit_na`
Mucosal chemokines (ccl25, ccl28, cxcl14, and cxcl17):
To look into genes correlated with scGPS scores:
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)df_Ciliopathy_cor_test<-as.data.frame(do.call(rbind, Ciliopathy_cor_test))
df_order_Ciliopathy_cor_test<-df_Ciliopathy_cor_test[order(unlist(df_Ciliopathy_cor_test$estimate),decreasing = TRUE),]tidy_Ciliopathy_cor<-cbind(df_order_Ciliopathy_cor_test$estimate,df_order_Ciliopathy_cor_test$p.value)colnames(tidy_Ciliopathy_cor)<-c("cor","p.value")tidy_Ciliopathy_cor
cor p.value
Zmynd10 0.546579 0
Pifo 0.5425509 0
Lrrc6 0.5384109 0
Drc1 0.5367434 0
Dcdc2a 0.4935992 0
Ccdc65 0.4863407 0
Ccdc103 0.4782748 0
Nek5 0.4693503 0
Pacrg 0.4567388 0
Traf3ip1 0.4471433 0
Poc1a 0.4238989 0
Fgfr1op 0.4206791 0
Tmem138 0.4191797 0
Ccdc114 0.4153728 0
Tctex1d2 0.4115401 0
Cenpf 0.407906 0
Cep83 0.4070513 0
Lrrc56 0.4045086 0
Ift74 0.400825 0
Cspp1 0.3996876 0
Nphp1 0.395385 0
Cep55 0.3951101 0
Ift57 0.391189 0
B9d1 0.3873108 0
Ift88 0.3869675 0
Rpgrip1l 0.3865638 0
Dync2h1 0.3857778 0
Ift172 0.381393 0
Ift80 0.3790754 0
Cep290 0.370438 0
Plk4 0.368893 0
Ift81 0.3683296 0
Wdr34 0.3670231 0
Arl6 0.3664624 0
Cep41 0.3654999 0
B9d2 0.3607634 0
Ift43 0.3601675 0
Tmem216 0.3597497 0
Wdr60 0.3579426 0
C2cd3 0.3569508 0
Dync2li1 0.3564985 0
Bbs5 0.3533716 0
Wdr35 0.349725 0
Tmem67 0.3472159 0
Spag1 0.3449336 0
Nek1 0.343219 0
Tmem107 0.342606 0
Wdpcp 0.3425418 0
Sclt1 0.3367607 0
Ift52 0.3292574 0
Wdr19 0.3243224 0
Kif11 0.3242046 0
Cc2d2a 0.3237188 0
2700049A03Rik 0.3222697 0
Tmem231 0.320627 0
Tmem17 0.3174036 0
Cep164 0.3159639 0
D430042O09Rik 0.3156484 0
Bbip1 0.3148218 0
2610301B20Rik 0.3137307 0
Mks1 0.3134762 0
Ift140 0.3058836 0
Armc9 0.3034685 0
Cep104 0.3006778 0
1810043G02Rik 0.3004568 0
Cep120 0.2943722 0
Pibf1 0.2876425 1.388627e-317
Bbs7 0.2867671 1.392889e-315
Sdccag8 0.2848902 2.576083e-311
Ift122 0.2829121 7.454808e-307
Ift27 0.2670399 2.307436e-272
Bbs2 0.263533 4.716935e-265
Lztfl1 0.2576021 6.101379e-253
Ofd1 0.2527573 2.793616e-243
Intu 0.2493308 1.423027e-236
Inpp5e 0.2490544 4.894807e-236
Bbs1 0.2472615 1.424276e-232
Mre11a 0.2471985 1.882916e-232
Tbc1d32 0.2461905 1.618676e-230
Arl3 0.2422522 4.807888e-223
Poc1b 0.2354816 1.634973e-210
Mapkbp1 0.2330952 3.435638e-206
Fuz 0.2324085 5.899545e-205
Tctn3 0.223917 5.0765e-190
Kif14 0.2098085 1.478587e-166
Pde6d 0.2009281 1.221831e-152
Notch1 0.1970841 8.116042e-147
Tctn1 0.1954091 2.56165e-144
Mkks 0.1951724 5.752278e-144
Kctd10 0.1907798 1.571154e-137
Nphp3 0.1855321 4.787185e-130
Ccdc28b 0.1776045 3.662696e-119
Nek8 0.1751189 7.45169e-116
Ttc21b 0.1615266 1.24399e-98
Pik3r4 0.1607992 9.445133e-98
Bbs12 0.1567567 6.200812e-93
Sall1 0.1512815 1.296525e-86
Gna12 0.1460858 7.835921e-81
Galnt11 0.1320623 2.849657e-66
Gli3 0.1303523 1.338961e-64
Ahi1 0.1281622 1.719821e-62
Tubgcp6 0.1242371 8.376372e-59
Bbs10 0.1201034 4.783023e-55
Sirt2 0.1090072 1.328542e-45
Qk 0.1084995 3.413186e-45
Wwtr1 0.1078645 1.104313e-44
Fan1 0.09629114 6.398198e-36
Sufu 0.08002379 2.726486e-25
Kif7 0.07953328 5.317248e-25
Glis2 0.0764992 3.024284e-23
Zfp423 0.03725636 1.354483e-06
Gucy2e 0.03294734 1.933375e-05 unlist(tidy_Ciliopathy_cor[,1])
Zmynd10.cor Pifo.cor Lrrc6.cor Drc1.cor Dcdc2a.cor Ccdc65.cor
0.54657904 0.54255092 0.53841095 0.53674336 0.49359917 0.48634066
Ccdc103.cor Nek5.cor Pacrg.cor Traf3ip1.cor Poc1a.cor Fgfr1op.cor
0.47827477 0.46935028 0.45673881 0.44714331 0.42389891 0.42067915
Tmem138.cor Ccdc114.cor Tctex1d2.cor Cenpf.cor Cep83.cor Lrrc56.cor
0.41917967 0.41537280 0.41154005 0.40790602 0.40705129 0.40450856
Ift74.cor Cspp1.cor Nphp1.cor Cep55.cor Ift57.cor B9d1.cor
0.40082497 0.39968759 0.39538503 0.39511007 0.39118900 0.38731078
Ift88.cor Rpgrip1l.cor Dync2h1.cor Ift172.cor Ift80.cor Cep290.cor
0.38696745 0.38656376 0.38577781 0.38139299 0.37907543 0.37043802
Plk4.cor Ift81.cor Wdr34.cor Arl6.cor Cep41.cor B9d2.cor
0.36889297 0.36832959 0.36702306 0.36646235 0.36549993 0.36076339
Ift43.cor Tmem216.cor Wdr60.cor C2cd3.cor Dync2li1.cor Bbs5.cor
0.36016751 0.35974972 0.35794263 0.35695076 0.35649847 0.35337160
Wdr35.cor Tmem67.cor Spag1.cor Nek1.cor Tmem107.cor Wdpcp.cor
0.34972497 0.34721589 0.34493360 0.34321905 0.34260603 0.34254181
Sclt1.cor Ift52.cor Wdr19.cor Kif11.cor Cc2d2a.cor 2700049A03Rik.cor
0.33676072 0.32925738 0.32432245 0.32420459 0.32371885 0.32226969
Tmem231.cor Tmem17.cor Cep164.cor D430042O09Rik.cor Bbip1.cor 2610301B20Rik.cor
0.32062701 0.31740361 0.31596392 0.31564838 0.31482181 0.31373074
Mks1.cor Ift140.cor Armc9.cor Cep104.cor 1810043G02Rik.cor Cep120.cor
0.31347622 0.30588356 0.30346851 0.30067775 0.30045684 0.29437219
Pibf1.cor Bbs7.cor Sdccag8.cor Ift122.cor Ift27.cor Bbs2.cor
0.28764247 0.28676713 0.28489023 0.28291214 0.26703987 0.26353297
Lztfl1.cor Ofd1.cor Intu.cor Inpp5e.cor Bbs1.cor Mre11a.cor
0.25760209 0.25275731 0.24933081 0.24905441 0.24726149 0.24719847
Tbc1d32.cor Arl3.cor Poc1b.cor Mapkbp1.cor Fuz.cor Tctn3.cor
0.24619054 0.24225224 0.23548163 0.23309521 0.23240846 0.22391701
Kif14.cor Pde6d.cor Notch1.cor Tctn1.cor Mkks.cor Kctd10.cor
0.20980850 0.20092807 0.19708409 0.19540907 0.19517236 0.19077984
Nphp3.cor Ccdc28b.cor Nek8.cor Ttc21b.cor Pik3r4.cor Bbs12.cor
0.18553207 0.17760446 0.17511892 0.16152657 0.16079922 0.15675672
Sall1.cor Gna12.cor Galnt11.cor Gli3.cor Ahi1.cor Tubgcp6.cor
0.15128147 0.14608581 0.13206233 0.13035234 0.12816225 0.12423714
Bbs10.cor Sirt2.cor Qk.cor Wwtr1.cor Fan1.cor Sufu.cor
0.12010344 0.10900717 0.10849955 0.10786448 0.09629114 0.08002379
Kif7.cor Glis2.cor Zfp423.cor Gucy2e.cor
0.07953328 0.07649920 0.03725636 0.03294734
#volc_Ciliopathy_cor<-mutate(tidy_Ciliopathy_cor,sig=ifelse(tidy_Ciliopathy_cor$p.value<0.01,"P_adj<0.01","Not Sig"))
df_tidy_ciliopathy_cor<-data.frame(matrix(unlist(tidy_Ciliopathy_cor), nrow=112, byrow=F),stringsAsFactors=FALSE)
colnames(df_tidy_ciliopathy_cor)<-c("cor","p.value")
df_tidy_ciliopathy_cor$gene<-rownames(tidy_Ciliopathy_cor)
unlist(x)[2:30]
Cenpf Cep55 Kif11 Tmem138 Plk4 Fgfr1op Dync2li1 Ift57 Ift52 Nphp1 Ift74 Arl6 Tctex1d2
0.9290814 0.9066218 0.8907960 0.8105366 0.8061935 0.8028617 0.7735900 0.7513981 0.7497918 0.7491373 0.7369705 0.7260828 0.7077285
Cspp1 Mkks Cep164 Bbip1 B9d1 Pde6d Ift43 Arl3 Kctd10 Tmem107 Sirt2 Qk Lztfl1
0.6831866 0.6693836 0.6540933 0.6537958 0.6483817 0.6434139 0.6353225 0.6175333 0.6173846 0.5910281 0.5530700 0.5435209 0.5248394
Armc9 Traf3ip1 Kif14
0.0000000 0.0000000 0.0000000 wt_mucosa_cluster<-data.frame(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_mucosa_cluster<-aggregate(wt_mucosa_cluster[, 1:301], list(wt_mucosa_cluster[,302]), median)
unlist(sort(aggre_wt_mucosa_cluster[aggre_wt_mucosa_cluster$Group.1==8,],decreasing = TRUE))[2:51]
Aurka Epcam Bub1b Ap1m2 Cdh2 Krt17 Lamb3 Erbb2 Fgfr3 Trp53 Dkc1
0.9465433 0.9464838 0.9262256 0.8715195 0.8370419 0.8083948 0.7931045 0.7698715 0.7693658 0.7673429 0.7644277
Dsp Ctnnb1 Cdh1 Tmem97 Mapk14 Rab25 Sdc1 Fgfr2 Rela Cdc42ep4 Nras
0.7623156 0.7584781 0.7551761 0.7547596 0.7376547 0.7327166 0.7238517 0.7090374 0.7087994 0.6999048 0.6965136
Pycard Med1 Zkscan3 Rnf6 Ece1 Yap1 Odc1 Klf6 Mark2 Smad7 Slc6a6
0.6929438 0.6918729 0.6917539 0.6882437 0.6864291 0.6695621 0.6653974 0.6621549 0.6598644 0.6582580 0.6565326
Bax Smad4 Stk11 Sar1b Dnajc10 Pdpn Nfkbiz Bcl10 Ndufa13 Rps3 Mfge8
0.6538256 0.6511483 0.6306521 0.6227094 0.5781473 0.5703832 0.5436994 0.5382853 0.5209127 0.5033615 0.5031830
Igf2 Akt1 Gnas Nfkbia Scgb3a2 Il17a
0.4425274 0.4134043 0.4093586 0.4013267 0.3237149 0.0000000 ---
title: "Trachea_WT_10x"
output: html_notebook
---
## This script analyze 10x scRNA-seq data from E15, E16, P1, and P4 wt mice
```{r}
library(dplyr)
library(Matrix)
require(devtools)
install_version("mvtnorm", version = "1.0-8", repos = "http://cran.us.r-project.org") ##Note that I need version 1.0-8 because the newest version requires R>3.5 (my r version is 3.4). mvtnorm is a dependency for fpc which is a dependency for Seurat.
library(Seurat)
```

##### First load E15 data (nGene 1k, nUMI 5k already applied)
```{r}
load("E15_Oct10_10X_Trachea.RData")
E15_Oct10_mm10.1.2.0_Trachea <- SetAllIdent(object = E15_Oct10_mm10.1.2.0_Trachea, id = "genotype")

```
##### second load seu_E16P1P4_wt.RData
```{r}
load("seu_E16P1P4_wt.RData")
```
##### data has been log normalized.
```{r}
seurat_E15E16P1P4_wt<-MergeSeurat(object1 = seu_E16P1P4_wt,object2 = SubsetData(object=E15_Oct10_mm10.1.2.0_Trachea,ident.use=c("wt")),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18")
```

##### Alternatively, seurat_E15E16P1P4_wt object can be created by merging 4 seurat objects in E15_Oct10_10X_Trachea.RData, E16_Dec7v3_Trachea.RData, seu_P1_Dec11_mm10.1.2.0.RData, and P4_10X_mm10_1.2.0.RData 

##### remove old analysis done on the E16P1P4 dataset:
```{r}
seurat_E15E16P1P4_wt@meta.data<-seurat_E15E16P1P4_wt@meta.data[,-which(names(seurat_E15E16P1P4_wt@meta.data) %in% c("res.0.8", "res.1.2","res.1.4","cell_type","S.Score","G2M.Score","Phase","old.ident","CellCycle_score","mocosaGoblet_score","ciliopathy_score","PCD_score"))]
```
##### Basic stats about the data:
```{r}
aggregate(seurat_E15E16P1P4_wt@meta.data[, c(1:2,8:9)], list(seurat_E15E16P1P4_wt@meta.data$seq_group), median)

```
```{r}
median(seurat_E15E16P1P4_wt@meta.data$nGene) 
```
```{r}
mean(seurat_E15E16P1P4_wt@meta.data$nGene) 
```
```{r}
median(seurat_E15E16P1P4_wt@meta.data$nUMI) 
```
```{r}
mean(seurat_E15E16P1P4_wt@meta.data$nUMI) 
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$age) 
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$seq_group) 
```

##### scale the data:
```{r}
seurat_E15E16P1P4_wt <- ScaleData(object = seurat_E15E16P1P4_wt)
```

##### find variable genes and run PCA:
```{r}
seurat_E15E16P1P4_wt <- FindVariableGenes(object = seurat_E15E16P1P4_wt, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```
```{r}
seurat_E15E16P1P4_wt <- RunPCA(object = seurat_E15E16P1P4_wt,pcs.compute = 25, do.print = FALSE)
seurat_E15E16P1P4_wt <- ProjectPCA(object = seurat_E15E16P1P4_wt, do.print = FALSE)
```
```{r,fig.height=20,fig.width=8}
PCHeatmap(object = seurat_E15E16P1P4_wt, pc.use = c(1:12), cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 30)

```

```{r}
PCElbowPlot(object = seurat_E15E16P1P4_wt,num.pc = 25)
```
##### clustering:
##### try resolution=0.8:
```{r}
n.pcs = 24
res.used <- 0.8

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.0.8")

```

##### resolution=1.4:
```{r}
n.pcs = 24
res.used <- 1.4

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

##### k.param=15 to have better distinction for rare cells:
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.4")

```


###### res1.6 leads to further specification of immune cells, P1 and P4 mesenchymal progenitor separation, and separation of E16 basal:
```{r}
n.pcs = 24
res.used <- 1.6

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.6")
```

##### We will use resolution of 1.4 for further analysis.

####--------------------------------------------------------------------#################

```{r}
#save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")  # this seurat object will be updated and saved as analysis goes
```

```{r}
#load("seuratE15E16P1P4_wt.RData")
```


```{r,fig.width=10,fig.height=5}

TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,pt.size = 0.2,group.by="age")

```
##### 2 E16 wt mice:
```{r,fig.width=10,fig.height=5}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,group.by="seq_group",pt.size = 0.2,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$age=="E16"])

```

##### 2 P1 wt mice:
```{r,fig.width=10,fig.height=5}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,group.by="seq_group",pt.size = 0.2,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$age=="P1"])

```


##### Annotate clusters by known markers:

```{r,fig.height=17,fig.width=50}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=30,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28,14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25,24,32)
  )
```

```{r,fig.height=17,fig.width=50}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,8,11,9,1,30,2,20,29,7,26,27,28)],genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=35,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28)
  )
```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

```{r,fig.height=6,fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type")

```
```{r, fig.height=5, fig.width=10}
FeaturePlot(seurat_E15E16P1P4_wt, c("Alas2","Hba-a2"),pt.size = 0.3, overlay=TRUE,no.legend=FALSE,cols.use=c("grey","red","blue","green"),max.cutoff = c(2.5,5))
```


```{r, fig.height=6, fig.width=12}
FeaturePlot(seurat_E15E16P1P4_wt, c("Tg","Pax8"),pt.size = 0.2,no.legend=FALSE,nCol = 2)
```
```{r, fig.height=5, fig.width=10}
FeaturePlot(seurat_E15E16P1P4_wt, c("Tg","Pax8"),pt.size = 0.3, overlay=TRUE,no.legend=FALSE,cols.use=c("grey","red","blue","green"),max.cutoff = c(2.5,1))
```

##### cluster annotation v1:
```{r}
library(plyr)
seurat_E15E16P1P4_wt@meta.data$cell_type<-mapvalues(seurat_E15E16P1P4_wt@meta.data$res.1.4,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33"),to=c("Fibroblast","Basal","Secretory","Basal","Fibroblast","Fibroblast","Fibroblast","Secretory","CyclingEpithelial","Transitional","Fibroblast","CyclingEpithelial","Fibroblast","Fibroblast","Immune","Fibroblast","Fibroblast","Fibroblast","Muscle","Fibroblast","Ciliated","Muscle","MesenchymalProgenitor","Endothelial","Thyroid","MesenchymalProgenitor","Neural/Schwann","Ciliated","CiliaSecretory","Basal","CyclingEpithelial","Endothelial","Doublet","MesenchymalProgenitor"))
```

##### use nonShh subset clustering information (seurat object: E15E16P1P4_wt_nonShh resolution=1.6) to annotate those non-epithelial cells:
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_nonShh_cellType1.6, col.name = "specific_type")
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type)
```

##### keep the epithelial cell annotation as in seurat_E15E16P1P4_wt@meta.data$cell_type:
```{r}
seurat_E15E16P1P4_wt@meta.data$specific_type <- ifelse(is.na(seurat_E15E16P1P4_wt@meta.data$specific_type), as.character(seurat_E15E16P1P4_wt@meta.data$cell_type), as.character(seurat_E15E16P1P4_wt@meta.data$specific_type))
```

##### now we have new (more detailed) annotation for all cells:
```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type)
```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$gate,useNA = "always")  
#gate <NA> means cells from E16 and P1 which did not go through FACS
```
##### specific_type of each cell is defined later.
```{r,fig.width=10,fig.height=5}

TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,pt.size = 0.2,group.by="specific_type")+scale_color_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080', '#ffffff'
))

```
```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("E15"),],aes(gate,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#4363d8', '#f58231', '#911eb4', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#800000', '#808000', '#ffd8b1', '#000075', '#808080', '#ffffff'
))  
```


```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("P4"),],aes(gate,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#fffac8', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```
```{r,fig.width=10,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data,aes(specific_type,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```



```{r,fig.width=5,fig.height=8}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("P1"),],aes(seq_group,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```
```{r,fig.width=5,fig.height=8}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("E16"),],aes(seq_group,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324','#000000', '#fffac8', '#800000', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```


```{r}
save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")
```

```{r,fig.height=17,fig.width=90}
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt5","Krt8","Scgb3a2","Spdef","Creb3l1","Gp2","Foxj1","Twist2","Wnt2","Ly6a","Thy1","Sox9","Acan","Acta2","Myh11","Rgs5","Notch3","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="specific_type",group.cex = 30,cex.row=30,cells.use = seurat_E15E16P1P4_wt@cell.names[!(seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Thyroid","Doublet","RBC"))],group.order = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC")
  )
```


##### find differentially expressed genes between clusters of interest:
```{r}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

E15E16P1P4_wt_res14_BasalSecretory<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(9,30),only.pos = TRUE)
E15E16P1P4_wt_res14_BasalSecretory
```

##### For the purpose of visualization, we average within each specific cell type:
```{r}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
average_wt_specific_Annotation<-AverageExpression(object = seurat_E15E16P1P4_wt,return.seurat = T)
```
```{r,fig.height=12,fig.width=15}

DoHeatmap(object = average_wt_specific_Annotation, genes.use = c("Epcam","Trp63","Krt5","Krt8","Scgb3a2","Spdef","Creb3l1","Gp2","Foxj1","Twist2","Wnt2","Ly6a","Thy1","Sox9","Acan","Acta2","Myh11","Rgs5","Notch3","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,cells.use = average_wt_specific_Annotation@cell.names[!(average_wt_specific_Annotation@cell.names %in% c("Thyroid","Doublet","RBC"))],group.order = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))
```

```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
# just to plot different cell types by a custom order:
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("lightgrey","red"),genes.plot = c("Ano1","Cftr","Lrrc8a","Clca3a1","Clca3a2","Clca3b","Clca4a","Clca4b","Clcc1","Clcn2","Clcn3","Clcn4","Clcn7","Clcnka","Clcnkb","Clca1","Clca2","Gabrp","Ano2","Clcn1","Clcn5","Clic3"),group.by = "ident", x.lab.rot = T,plot.legend = T) # other chloride channels
```
```{r}
print(levels(seurat_E15E16P1P4_wt@ident))
```

```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("lightgrey","red"),genes.plot = c("Mki67","Epcam","Trp63","Muc5ac","Spdef","Creb3l1","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Acta2","Myh11","Rgs5","Notch3","Wnt2","Ly6a","Thy1","Cd34","Acan","Col2a1","Twist2"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### epithlial cells across ages:
```{r,fig.height=5, fig.width=15}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Trp63","Spdef","Creb3l1","Foxj1","CellCycle_score"), nCol = 5,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```

```{r,fig.height=5, fig.width=8}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cftr","Ano1"), nCol = 2,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```

```{r}
df_wt<-FetchData(seurat_E15E16P1P4_wt,c("Ano1","Cftr","Krt4","Krt13","res.1.4","age","seq_group","cell_type","specific_type"))

```

```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(specific_type,Cftr))+facet_grid(.~age)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```
```{r, fig.height=3, fig.width=3}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Cftr))+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.07,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```
```{r, fig.height=3, fig.width=3}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Ano1))+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.07,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```
```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(specific_type,Ano1))+facet_grid(.~age)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```

```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Ano1))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```

##### any possible ionocytes?:
```{r,fig.height=5, fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cftr","Foxi1"), nCol = 2,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```
```{r}
table(df_all_wt$res.1.4[df_all_wt$Foxi1>0])

```

```{r}
table(df_all_wt$res.1.4[df_all_wt$Cftr>0 & df_all_wt$Foxi1>0])

```
##### epithelial cell composition across ages:
```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)],seurat_E15E16P1P4_wt@meta.data$cell_type[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)])
```

##### a few lineage markers:
```{r,fig.height=20,fig.width=20}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Sox10","Mpz","Shh","Phox2a","Phox2b"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type")

```

```{r,fig.height=8,fig.width=22}
seurat_E15E16P1P4_wt=buildClusterTree(seurat_E15E16P1P4_wt,do.reorder = F,reorder.numeric = F,pcs.use = 1:24)

```

##### potential mesenchymal progenitors:
```{r}
E15E16P1P4_wt_res14_22over33<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(22),ident.2 = c(33),only.pos = TRUE)
E15E16P1P4_wt_res14_22over33
```
```{r}
E15E16P1P4_wt_res14_33over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(33),ident.2 = c(22),only.pos = TRUE)
E15E16P1P4_wt_res14_33over22
```
```{r}
E15E16P1P4_wt_res14_25over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(25),ident.2 = c(22),only.pos = TRUE)
E15E16P1P4_wt_res14_25over22
```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Piezo2"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=4,fig.width=47}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Sox9","Acan","Col2a1","Piezo2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25)],group.order = c(10,33,12,6,0,22,5,4,13,18,17,25,15,19,21,16,14,31,23)
  )
```

```{r,fig.height=15,fig.width=46}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Wnt2","Cd34","Prmt1","Egfl6","Crabp2","Piezo2","Reg3g","Ybx3","Klf9","Zfp36","Lpl","Ptrf","Ly6a","Tppp3","Anxa1","Ly6c1","Pi16","Thy1","Anxa3","Ccl7","Ccl2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(33,22,25)],group.order = c(33,22,25)
  )
```

```{r,fig.height=15,fig.width=46}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Wnt2","Cd34","Prmt1","Egfl6","Crabp2","Piezo2","Sostdc1","Ybx3","Klf9","Zfp36","Ptrf","Lpl","Ly6a","Tppp3","Ly6c1","Pi16","Thy1","Anxa3","Anxa1","Ccl7","Ccl2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="age",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(33,22,25)]
  )
```

##### scGPS (scoring):

```{r}
geneList<-read.csv(file = "GenesOMIM_all.txt",header=T,sep="\t",stringsAsFactors = F) 
# Genes_scGPS_all.txt and GenesOMIM_all.txt are essentially the same. Both files can be used here.
geneList<-lapply(geneList,function(x) unlist(strsplit(unlist(x),split=","))) 
head(geneList$Mucociliary)
```


```{r}
library(dplyr)
percentile_table_E15E16P1P4wt<-apply(seurat_E15E16P1P4_wt@data,1,percent_rank)
```
```{r}
percentile_table_E15E16P1P4wt[1:6,1:6]  #just to take a look at the percent_rank

```
```{r}
 CellCycle_score_wt<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Cell_cycle],1,mean)
```
```{r}
 head( CellCycle_score_wt)
```
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = CellCycle_score_wt, col.name = "CellCycle_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("CellCycle_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r}
wt_mocosaGoblet_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],1,mean)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_mocosaGoblet_score, col.name = "mucosaGoblet_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4",do.sort = T)

```


```{r}
 wt_ciliopathy_table<- percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Ciliopathy]
```
```{r}
 wt_ciliopathy_score<- apply(wt_ciliopathy_table,1,mean)
```
```{r}
 head(wt_ciliopathy_score)
```
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_ciliopathy_score, col.name = "ciliopathy_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("ciliopathy_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r}
 wt_PCD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Primary.ciliary.dyskinesia],1,mean)
```
```{r}
 head(wt_PCD_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_PCD_score, col.name = "PCD_score")

```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```

```{r, fig.height=5, fig.width=5}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Ciliated","CiliaSecretory"),],aes(age,PCD_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```




```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("CiliaSecretory"))

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```
```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("Secretory"))

```
```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("CiliaSecretory"))

```

```{r, fig.height=5, fig.width=5}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory"),],aes(age,mucosaGoblet_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```

```{r}
 wt_bronchitis_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Bronchiectasis.and.Bronchitis],1,mean)
```
```{r}
 head(wt_bronchitis_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_bronchitis_score, col.name = "Bronchiectasis_Bronchitis")

```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```



```{r}
 wt_COPD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$COPD],1,mean)
```
```{r}
 head(wt_COPD_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_COPD_score, col.name = "COPD")

```


```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("COPD"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```

```{r}
 wt_asthma_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Pulmonary...Asthma],1,mean)
```
```{r}
 head(wt_asthma_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_asthma_score, col.name = "Asthma")

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Asthma"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```
```{r, fig.height=5, fig.width=7}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory","Immune_1"),],aes(age,Asthma))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.005,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```

##### check some human GWAS:

```{r,fig.height=6,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Apip","Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```


##### CF GWAS:
```{r,fig.height=4,fig.width=6}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Aplp1","Aplp2","H2-Aa","H2-Ab1","H2-Eb1","H2-Eb2"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### COPD:
```{r,fig.height=4,fig.width=9}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### Developmental lanscape:
##### basal cells across time:
```{r}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

E15E16P1P4_wt_res14_3over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(3),ident.2 = c(1),only.pos = TRUE)
E15E16P1P4_wt_res14_3over1
```

```{r}
write.table(E15E16P1P4_wt_res14_3over1,"E15E16P1P4_wt_res14_3over1.txt",sep="\t")

```


```{r}
E15E16P1P4_wt_res14_1over3<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(1),ident.2 = c(3),only.pos = TRUE)
E15E16P1P4_wt_res14_1over3
```

```{r}
write.table(E15E16P1P4_wt_res14_1over3,"E15E16P1P4_wt_res14_1over3.txt",sep="\t")

```


```{r}
E15E16P1P4_wt_res14_29over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(29),ident.2 = c(1),only.pos = TRUE)
E15E16P1P4_wt_res14_29over1
```

```{r,fig.height=15,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt6","Wnt4","Bmp7","Krt15","Krt5","Cyp2f2","Lbp","BC048546","Ly6e","Aqp3","Aqp4","Wnt4","Retnla","Itln1","Ltf","Scgb1a1","Aqp5","Errfi1","Epas1","Rpl9-ps6","Rpl13-ps3","Rpl6l"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,8,11,9,1,30,2,20,29,7,26,27,28,24)],group.order = c(3,8,11,9,1,30,2,20,29,7,27,28,26,24)
  )
```

```{r,fig.height=28, fig.width=12}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Aqp1","Aqp2","Aqp3","Aqp4","Aqp5","Aqp6","Aqp7","Aqp8","Aqp9","Aqp11","Aqp12"), nCol = 1,x.lab.rot = T,point.size.use = 0.2)
```

```{r,fig.height=6,fig.width=20}
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Krt5","Krt15","Krt14","Wnt4","Bmp7","Aqp3","Aqp4","Aqp5","Errfi1","Rpl6l","Rpl9-ps6","Rpl13-ps3"),
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=25,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29)],group.order = c(3,1,29)
  )
```
```{r}
df_3_1_29_wt<-FetchData(seurat_E15E16P1P4_wt,c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt4","Krt5","Krt15","Aqp3","Aqp4","Aqp5","Rpl6l","Rpl9-ps6","Rpl13-ps3","age","res.1.4"))
df_3_1_29_wt<-df_3_1_29_wt[order(factor(df_3_1_29_wt$res.1.4,levels=c("3","1","29")),df_3_1_29_wt$age),]
```
```{r}
age<-substr(rownames(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),]),1,3)

```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

```

```{r}
library(gplots)
library(viridis)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r}
library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r, fig.height=3, fig.width=7}

pdf(file = paste("Manuscript/heatmap/","basal_dev",".pdf", sep = ""), width = 7, height = 3)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
dev.off()

```


```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))+scale_x_discrete(limits=c("3","1","29"))
```

##### ciliated cells across time:
```{r}
E15E16P1P4_wt_res14_27over20<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(27),ident.2 = c(20),only.pos = TRUE)
E15E16P1P4_wt_res14_27over20
```
```{r}
write.table(E15E16P1P4_wt_res14_27over20,"E15E16P1P4_wt_res14_27over20.txt",sep="\t")

```
```{r}
E15E16P1P4_wt_res14_20over27<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(20),ident.2 = c(27),only.pos = TRUE)
E15E16P1P4_wt_res14_20over27
```
```{r}
write.table(E15E16P1P4_wt_res14_20over27,"E15E16P1P4_wt_res14_20over27.txt",sep="\t")

```

```{r,fig.height=20,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Cox6b2","Cks2","H2afx","Ccna1","Ccno","Plk4","Cdc20b","Cdc20","Foxn4","Shisa8","Hyls1","Smim24","Mcidas","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Basp1","Fam166b","Ifitm1","Ly6a","Ly6e","Ly6c1","Adam8","Cxcl17","Aoc1","Retnla","Itln1","Ltf","Prr18"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],group.order = c(20,27)
  )
```

```{r,fig.height=20,fig.width=40}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)
```

```{r}
df_20_27_wt<-FetchData(seurat_E15E16P1P4_wt,c("Foxj1","Lrrc23","Ccdc67","Cep152","Plk4","Ccna1","Ccno","Cdc20b","Cdc20","Hyls1","Mcidas","Smim24","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Ly6a","Ly6c1","Adam8","Cxcl17","Ifitm1","age","res.1.4"))
df_20_27_wt<-df_20_27_wt[order(df_20_27_wt$res.1.4,df_20_27_wt$age),]
```
```{r}
age<-substr(rownames(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),]),1,3)

```


```{r}
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:25])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```
```{r}
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:22])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ 
  scale_x_discrete(limits=c("20","27"))+
  scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))
```

```{r,fig.width=5,fig.height=5}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)])
```
```{r,fig.width=5,fig.height=5}
t_ciliated<-as.data.frame(prop.table(table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)]),1))
```
```{r,fig.width=3,fig.height=2}
ggplot(data=t_ciliated,aes(x=Var1,y=Freq,group=Var2,colour=Var2))+geom_line()+geom_point()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r,fig.width=3,fig.height=2}
ggplot(data=t_ciliated,aes(x=substr(Var1,1,1),y=Freq,group=Var2))+geom_line(aes(colour=Var2),linetype="dotted")+geom_point(aes(shape=Var1))+ theme(axis.text.x = element_text(angle = 45, hjust = 0.7))+
  scale_shape_manual(values = c("square open", "cross","diamond open","plus")) 
```

##### secretory cells across time:
```{r}
E15E16P1P4_wt_res14_2over7<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(2),ident.2 = c(7),only.pos = TRUE)
E15E16P1P4_wt_res14_2over7
```
```{r}
E15E16P1P4_wt_res14_7over2<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(7),ident.2 = c(2),only.pos = TRUE)
E15E16P1P4_wt_res14_7over2
```

```{r,fig.height=8,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5ac","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7)]
  )
```
```{r}
df_2_7_wt<-FetchData(seurat_E15E16P1P4_wt,c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1","age","res.1.4"))
df_2_7_wt<-df_2_7_wt[order(df_2_7_wt$res.1.4,df_2_7_wt$age),]
```
```{r}
age<-substr(rownames(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),]),1,3)

```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

```

```{r}
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```
```{r}
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ 
  scale_x_discrete(limits=c("2","7"))+
  scale_fill_manual(values = c("#2ca25f","#66c2a4","#99d8c9"))
```


```{r,fig.height=8,fig.width=8}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Foxj1","Mcidas","Cdhr3","Creb3l1","Spdef","Gp2"), nCol = 3,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )

```
```{r,fig.height=4,fig.width=3}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )

```
##### immune response:
##### Toll like receptors and signaling:
```{r,fig.height=40,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Tlr1","Tlr2","Tlr3","Tlr4","Tlr5","Tlr6","Tlr7","Tlr8","Tlr9","Tlr11","Tlr12","Tlr13","Myd88","Ticam1"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

#####CLRs (C-type lectin domain): 
```{r,fig.height=10,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Reg3a","Reg3b","Reg3g","Clec4n"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
##### NLR:
```{r,fig.height=6,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Nod1","Nod2","Nlrp6"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
##### RLR:
```{r,fig.height=6,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Ddx58","Ifih1","Dhx58"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```


```{r,fig.height=20,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cd177","Ptgdr","F2rl1","Tslp","Il33","Il25","Ager"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=18,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Defb1","Lyz1","Lyz2","Ltf","Camp","Sftpa1","Sftpd","Slpi"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```



```{r}
df_all_wt<-FetchData(seurat_E15E16P1P4_wt,c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1","res.1.4","age","seq_group","specific_type","Foxj1","Spdef","Foxa3","Creb3l1","Gp2","Tff2","Scgb1a1","Cftr","Foxi1","Gja1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4","Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1","Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg","Il2","Il17b","Il17d","Il34","Il11","Il15","Ifnk","Ifnlr1","Nfkbiz","Nfkbia"))

```
```{r}
table(colnames(df_all_wt))
```

```{r, fig.height=3, fig.width=7}
#for (i in c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1"))
#{
#pdf(file = paste("Manuscript/MicrobialSensing/",i,".pdf", sep = ""), width = 7, height = 3)
#print(ggplot(df_all_wt[(df_all_wt$specific_type %in% #c("Basal","Secretory","Ciliated","Transitional","CiliaSecretory","CyclingEpithelial")),],aes_string(x="age",y=i))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.#01,stackdir="center",position=position_dodge(0.8), dotsize=0.8)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
#                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1)))
#dev.off()
#}
```

##### Some categories of genes:
##### lipid synthetic enzymes or inhibitors: "Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Hpgds","Ptgr2","Tbxas1","Alox5","Ptgdr"
##### known Th2 inducers, effectors, and mediators:"Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg"
##### chemokines: Pbpb not expressed, Ccl21c, Ccl21c.1, Cxcl11 not found in mm10.1.2.0. "Cxcl3","Cxcl2","Cxcl1","Pf4","Cxcl5","Cxcl9","Cxcl10","Cxcl12","Cxcl13","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl1","Ccl2","Ccl3","Ccl4","Ccl5","Ccl7","Ccl8","Ccl11","Ccl12","Ccl9","Ccl17","Ccl19","Ccl20","Ccl21a","Ccl21b.1","Ccl22","Ccl6","Ccl24","Ccl25","Ccl27a","Ccl27b","Ccl28","Xcl1","Cx3cl1"
##### expressed chemokines: "Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1"
##### Interleukins (Il3,Il9,Il20,Il31 not expressed):"Il10","Il11","Il12a","Il12b","Il13","Il15","Il16","Il17a","Il17b","Il17c","Il17d","Il17f","Il18","Il19","Il2","Il21","Il22","Il24","Il25","Il27","Il33","Il34","Il4","Il5","Il6","Il7","Il1a","Il1b"
##### Antimicrobial effectors: "Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4"
##### known epithelial cytokines and mediators: "Il1a","Il25","Il33","Tslp","Csf2","Ccl5","Cxcl10"
##### Most interferons cannot be found in our dataset:

```{r,fig.height=4, fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Ifnk","Ifnlr1"),group.by="age", legend.position = "left")
```
```{r}
seurat_E15E16P1P4_wt@meta.data$type_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$cell_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
```
```{r}
seurat_E15E16P1P4_wt@meta.data$specificType_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
```


```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
print(levels(seurat_E15E16P1P4_wt@ident))
```

```{r,fig.height=10,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specificType_age")
seurat_E15E16P1P4_wt@ident=factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(16:19,1:4,66,59:61,12:15,9:11,30:37)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("gray","forestgreen"),genes.plot = rev(c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g")),group.by = "ident", x.lab.rot = T,plot.legend = T,do.return = T)+rotate()+ theme(axis.text.x = element_text(angle = 45, vjust = 1,hjust=1)) #this scales both genotypes together
```

##### Mucosal chemokines (ccl25, ccl28, cxcl14, and cxcl17):
```{r, fig.height=3, fig.width=8}
library(ggpubr)

ggplot(df_all_wt[(df_all_wt$specific_type %in% c("Basal","Transitional","Ciliated","Secretory","CiliaSecretory","CyclingEpithelial")),],aes(age,Cxcl17))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```


##### To look into genes correlated with scGPS scores:
```{r}
Ciliopathy_cor_test<-apply(wt_ciliopathy_table, 2, function(x) cor.test(seurat_E15E16P1P4_wt@meta.data$ciliopathy_score,x,method="pearson")) 
```
```{r}
df_Ciliopathy_cor_test<-as.data.frame(do.call(rbind, Ciliopathy_cor_test))

```
```{r}
df_order_Ciliopathy_cor_test<-df_Ciliopathy_cor_test[order(unlist(df_Ciliopathy_cor_test$estimate),decreasing = TRUE),]
```
```{r}
tidy_Ciliopathy_cor<-cbind(df_order_Ciliopathy_cor_test$estimate,df_order_Ciliopathy_cor_test$p.value)
```

```{r}
colnames(tidy_Ciliopathy_cor)<-c("cor","p.value")
```

```{r}
tidy_Ciliopathy_cor
```
```{r}
unlist(tidy_Ciliopathy_cor[,1])

```

```{r}

#volc_Ciliopathy_cor<-mutate(tidy_Ciliopathy_cor,sig=ifelse(tidy_Ciliopathy_cor$p.value<0.01,"P_adj<0.01","Not Sig"))
df_tidy_ciliopathy_cor<-data.frame(matrix(unlist(tidy_Ciliopathy_cor), nrow=112, byrow=F),stringsAsFactors=FALSE)
colnames(df_tidy_ciliopathy_cor)<-c("cor","p.value")
df_tidy_ciliopathy_cor$gene<-rownames(tidy_Ciliopathy_cor)
```
```{r}
library(ggrepel)
ggplot(df_tidy_ciliopathy_cor, aes(cor, p.value))+geom_point() +geom_text_repel(data=filter(df_tidy_ciliopathy_cor,cor>0.45), aes(label=gene))+ylim(-2e-05,2.5e-05)
p
p+geom_text_repel(data=filter(as.data.frame(tidy_Ciliopathy_cor), p_val_adj<0.05), aes(label=rownames(tidy_Ciliopathy_cor)))
```
```{r}
wt_ciliopathy_cluster<-data.frame(wt_ciliopathy_table,seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_ciliopathy_cluster<-aggregate(wt_ciliopathy_cluster[, 1:112], list(wt_ciliopathy_cluster[,113]), median)
#aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,][order(aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,],decreasing = TRUE)]
```
```{r}
x<-sort(aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,],decreasing = TRUE)
unlist(x)[2:30]
```
```{r}
wt_mucosa_cluster<-data.frame(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_mucosa_cluster<-aggregate(wt_mucosa_cluster[, 1:301], list(wt_mucosa_cluster[,302]), median)

```
```{r}
unlist(sort(aggre_wt_mucosa_cluster[aggre_wt_mucosa_cluster$Group.1==8,],decreasing = TRUE))[2:51]
```











